Note: Descriptions are shown in the official language in which they were submitted.
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METHOD AND SYSTEM OF DETECTING AND USING GEOFENCING FOR
FRAUD DETECTION AND MODELING
FIELD
[0001]The present disclosure relates to the use of geofences to identify fraud
factors, specifically using home markets and geolocation to identify when a
consumer is outside of a home market to identify fraud factors to provide
additional
account security.
BACKGROUND
[0002]When a fraudulent financial transaction occurs using a payment account,
such as a credit card account, it can often cause harm to not only the account
holder, but also to the merchant involved in the financial transaction and the
issuer
of the payment account. As such, many issuers offer services to their
customers to
help prevent fraud or to more quickly identify when a fraudulent transaction
occurs,
in order to remedy the situation. Many merchants also take measures to try and
prevent such fraudulent transactions from taking place. In one such solution,
issuers deny authorization for financial transactions when a transaction
originates in
a different state than the one where the account holder is based.
[0003]However, such solutions may often not account for consumers who
regularly
travel, live near state or municipal borders, or have multiple residences. In
these
instances, a consumer may have to repeatedly contact their issuer when moving
locations or may have to make special arrangements, such as having a separate
payment account in each location. Accordingly, current systems and methods for
identifying fraud factors, rules, and models are unsuitable for these types of
consumers.
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[0004]Thus, there is a need for a technical solution to improve the
identification of
fraud factors for consumers based on home markets and consumer geographic
locations.
SUMMARY
[0005]The present disclosure provides a description of a systems and methods
for
the identification of fraud factors by detecting and using geofences.
[0006]A method for identifying fraud factors includes: storing, in a fraud
database,
a plurality of data points; storing, in a transaction database, a plurality of
financial
transactions; storing, in a consumer database, a plurality of consumer data
entries,
wherein each consumer data entry is associated with a consumer and includes at
least a mobile device identifier and an account identifier; identifying, by a
processing device, at least one home market for each consumer data entry in
the
plurality of consumer data entries based on usage patterns of a financial
account
associated with the account identifier; associating, in the consumer database,
the
identified at least one home market with the corresponding consumer data
entry;
identifying, at a predetermined time, a geographic location of a mobile device
associated with the mobile device identifier for each consumer data entry;
storing,
in the fraud database, a location data point for each identified geographic
location if
the identified geographic location is outside of a predetermined distance from
the at
least one home market associated with the corresponding consumer data entry,
wherein the location data point includes at least the identified geographic
location,
the consumer associated with the corresponding consumer data entry, and a time
and/or date at which the geographic location was identified; and identifying,
by the
processing device, at least one fraud factor for detecting fraud in a
financial
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transaction, wherein the at least one fraud factor is based on at least the
plurality of
financial transactions, the plurality of data points, and each location data
point.
[0007] Another method for identifying fraud factors includes: storing, in a
fraud
database, a plurality of data points; storing, in a transaction database, a
plurality of
financial transactions; storing, in a consumer database, a plurality of
consumer data
entries, wherein each consumer data entry is associated with a consumer and
includes at least a mobile device identifier and at least one home market;
transmitting, by a transmitting device, the plurality of consumer data
entries;
receiving, by a receiving device, a plurality of location data points, wherein
each
location data point includes at least a mobile device identifier, a geographic
location
of a mobile device associated with the mobile device identifier, and a time
and/or
date at which the mobile device is identified as outside of a predetermined
distance
from the at least one home market associated with the corresponding consumer
data entry; and identifying, by a processing device, at least one fraud factor
for
detecting fraud in a financial transaction, wherein the at least one fraud
factor is
based on at least the plurality of financial transactions, the plurality of
data points,
and each location data point.
[0008] A system for identifying fraud factors includes a fraud database, a
transaction database, a consumer database, and a processing device. The fraud
database is configured to store a plurality of data points. The transaction
database
is configured to store a plurality of financial transactions. The consumer
database
is configured to store a plurality of consumer data entries, wherein each
consumer
data entry is associated with a consumer and includes at least a mobile device
identifier and an account identifier. The processing device is configured to:
identify
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at least one home market for each consumer data entry in the plurality of
consumer
data entries based on usage patterns of a financial account associated with
the
account identifier; associate, in the consumer database, the identified at
least one
home market with the corresponding consumer data entry; identify, at a
predetermined time, a geographic location of a mobile device associated with
the
mobile device identifier for each consumer data entry; store, in the fraud
database,
a location data point for each identified geographic location if the
identified
geographic location is outside of a predetermined distance from the at least
one
home market associated with the corresponding consumer data entry, wherein the
location data point includes at least the identified geographic location, the
consumer associated with the corresponding consumer data entry, and a time
and/or date at which the geographic location was identified; and identify at
least
one fraud factor for detecting fraud in a financial transaction, wherein the
at least
one fraud factor is based on at least the plurality of financial transactions,
the
plurality of data points, and each location data point.
[0009]Another system for identifying fraud factors includes a fraud database,
a
transaction database, a consumer database, a transmitting device, a receiving
device, and a processing device. The fraud database is configured to store a
plurality of data points. The transaction database is configured to store a
plurality
of financial transactions. The consumer database is configured to store a
plurality
of consumer data entries, wherein each consumer data entry is associated with
a
consumer and includes at least a mobile device identifier and at least one
home
market. The transmitting device is configured to transmit the plurality of
consumer
data entries. The receiving device is configured to receive a plurality of
location
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data points, wherein each location data point includes at least a mobile
device
identifier, a geographic location of a mobile device associated with the
mobile device
identifier, and a time and/or date at which the mobile device is identified as
outside of
a predetermined distance from the at least one home market associated with the
5 corresponding consumer data entry. The processing device is configured to
identify
at least one fraud factor for detecting fraud in a financial transaction,
wherein the at
least one fraud factor is based on at least the plurality of financial
transactions, the
= plurality of data points, and each location data point.
[0009a] According to one aspect of the present invention, there is provided a
computer-implemented method for identifying fraud factors, comprising:
receiving, by
a receiving device and from a data provider entity device, a plurality of data
points;
storing, in a fraud database, the plurality of data points; receiving, by the
receiving
device and from a financial transaction processing entity device, a plurality
of
financial transactions; storing, in a transaction database, the plurality of
financial
transactions; receiving, by the receiving device, consumer data for a
plurality of
consumers, comprising a plurality of mobile device identifiers and a plurality
of
account identifiers associated with the plurality of consumers; storing, in a
consumer
database, a plurality of consumer data entries, wherein each consumer data
entry is
associated with a consumer of the plurality of consumers and includes at least
a
mobile device identifier of the plurality of mobile device identifiers and an
account
identifier of the plurality of account identifiers; identifying, by a
processing device, at
least one home market for each consumer data entry in the plurality of
consumer
data entries based on usage patterns of a financial account associated with
the
account identifier; associating, in the consumer database, the identified at
least one
home market with the corresponding consumer data entry; identifying, at a
predetermined time, a geographic location of a mobile device associated with
the
mobile device identifier for each consumer data entry, wherein the geographic
location of the mobile device is received from a device of a location
identifying
agency; determining, by the processing device, that at least one identified
geographic
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location is located outside of a predetermined distances from the at least one
home
market associated with the corresponding consumer data entry; storing, in the
fraud
database, a location data point for each identified geographic location
outside of a
predetermined distance from the at least one home market associated with the
corresponding consumer data entry, wherein the location data point includes at
least
the identified geographic location, the consumer associated with the
corresponding
consumer data entry, and a time and/or date at which the geographic location
was
identified; identifying, by the processing device, at least one fraud factor
for detecting
fraud in an electronic financial transaction, wherein the at least one fraud
factor is
based on at least the plurality of financial transactions, the plurality of
data points,
and each location data point, and the electronic financial transaction is to
either be
permitted or denied based at least in part upon the identified at least one
fraud factor;
receiving, by the receiving device, an authorization request for the
electronic financial
transaction, wherein the authorization request includes transaction
information; and
transmitting, by a transmitting device, an authorization response message
indicating
that processing of the electronic financial transaction has been permitted or
denied.
[0009b] According to another aspect of the present invention, there is
provided a
computer-implemented method for identifying fraud factors, comprising:
receiving, by
a receiving device and from a data provider entity device, a plurality of data
points;
storing, in a fraud database, plurality of data points; receiving, by the
receiving device
and from a financial transaction processing entity device, a plurality of
financial
transactions; storing, in a transaction database, the plurality of financial
transactions;
receiving, by the receiving device, consumer data for a plurality of
consumers,
comprising a plurality of mobile device identifiers and a plurality of home
markets
associated with the plurality of consumers; storing, in a consumer database, a
plurality of consumer data entries, wherein each consumer data entry is
associated
with a consumer of the plurality of consumers and includes at least a mobile
device
identifier of the plurality of mobile device identifiers and at least one home
market;
transmitting, by a transmitting device and to a location identification agency
device,
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the plurality of consumer data entries; receiving, by the receiving device and
from the
location identification agency device, a plurality of location data points,
wherein each
location data point includes at least a mobile device identifier, a geographic
location
of a mobile device associated with the mobile device identifier, and a time
and/or date
at which the mobile device is identified as outside of a predetermined
distance from
the at least one home market associated with the corresponding consumer data
entry; identifying, by a processing device, at least one fraud factor for
detecting fraud
in an electronic financial transaction, wherein the at least one fraud factor
is based on
at least the plurality of financial transactions, the plurality of data
points, and each
location data point, and the electronic financial transaction is either
permitted or
denied based at least in part upon the identified at least one fraud factor;
receiving,
by the receiving device, an authorization request for the electronic financial
transaction, wherein the authorization request includes transaction
information;
transmitting, by a transmitting device, an authorization response message
indicating
that processing of the electronic financial transaction has been permitted or
denied.
[0009c] According to still another aspect of the present invention, there is
provided a
system for identifying fraud factors, comprising: a receiving device
configured to:
receive, from a data provider entity device, a plurality of data points,
receive, from a
financial transaction processing entity device, a plurality of financial
transactions, and
receive consumer data for a plurality of consumers, comprising a plurality of
mobile
device identifiers and a plurality of account identifiers associated with the
plurality of
consumers; a fraud database configured to store the plurality of data points;
a
transaction database configured to store the plurality of financial
transactions; a
consumer database configured to store the plurality of consumer data entries,
wherein each consumer data entry is associated with a consumer of the
plurality of
consumers and includes at least a mobile device identifier of the plurality of
mobile
device identifiers and an account identifier of the plurality of account
identifiers; and a
processing device configured to: identify at least one home market for each
consumer data entry in the plurality of consumer data entries based on usage
=
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5c
patterns of a financial account associated with the account identifier,
associate, in the
consumer database, the identified at least one home market with the
corresponding
consumer data entry, identify, at a predetermined time, a geographic location
of a
mobile device associated with the mobile device identifier for each consumer
data
entry, wherein the geographic location of the mobile device is received from a
device
of a location identifying agency, store, in the fraud database, a location
data point for
each identified geographic location outside of a predetermined distance from
the at
least one home market associated with the corresponding consumer data entry,
wherein the location data point includes at least the identified geographic
location,
the consumer associated with the corresponding consumer data entry, and a time
and/or date at which the geographic location was identified, and identify at
least one
fraud factor for detecting fraud in an electronic financial transaction,
wherein the at
least one fraud factor is based on at least a plurality of financial
transactions, the
plurality of data points, and each location data point, and the electronic
financial
transaction is either permitted or denied based at least in part upon the
identified at
least one fraud factor.
[0009d] According to yet another aspect of the present invention, there is
provided a
system for identifying fraud factors, comprising: a receiving device
configured to:
receive, from a data provider entity device, a plurality of data points,
receive, from a
financial transaction processing entity device, a plurality of financial
transactions,
receive consumer data for a plurality of consumers, comprising a plurality of
mobile
device identifiers and a plurality of account identifiers associated with the
plurality of
consumers; a fraud database configured to store the plurality of data points;
a
transaction database configured to store the plurality of financial
transactions; a
consumer database configured to store the plurality of consumer data entries,
wherein each consumer data entry is associated with a consumer of the
plurality of
consumers and includes at least a mobile device identifier of the plurality of
mobile
device identifiers_and at least one home market; a transmitting device
configured to
transmit, to a location identification device, the plurality of consumer data
entries; and
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a processing device configured to identify at least one fraud factor for
detecting fraud
in a financial transaction, wherein the at least one fraud factor is based on
at least the
plurality of financial transactions, the plurality of data points, and each
location data
point; wherein the receiving device is further configured to receive, from the
location
identification device, the plurality of location data points, wherein each
location data
point includes a mobile device identifier, a geographic location of a mobile
device
associated with the mobile device identifier, and a time and/or date at which
the
mobile device is identified as outside of a predetermined distance from the at
least
one home market associated with corresponding consumer data entry.
BRIEF DESCRIPTION OF THE DRAWING FIGURES
[0010] The scope of the present disclosure is best understood from the
following
detailed description of exemplary embodiments when read in conjunction with
the
accompanying drawings. Included in the drawings are the following figures:
[0011] FIG. 1 is a high level diagram illustrating a system for identifying
fraud factors
using home markets and geographic locations in accordance with exemplary
embodiments.
[0012] FIG. 2 is a block diagram illustrating the fraud factor processing
server of the
system of FIG. 1 in accordance with exemplary embodiments.
[0013] FIG. 3 is a block diagram illustrating the consumer database of the
fraud
factor processing server of FIG. 2 in accordance with exemplary embodiments.
[0014] FIG. 4 is a diagram illustrating home markets associated with a
consumer in
accordance with exemplary embodiments.
[0015] FIG. 5 is a diagram illustrating identified geographic locations for
consumers
used to identify fraud factors in accordance with exemplary embodiments.
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[0016] FIG. 6 is a flow diagram illustrating a method for identifying location
data
points for use in identifying fraud factors in accordance with exemplary
embodiments.
[0017] FIG. 7 is a flow diagram illustrating a method for identifying and
applying
fraud factors based on location data points in accordance with exemplary
embodiments.
[0018] FIGS. 8 and 9 are flow charts illustrating exemplary methods for
identifying
fraud factors in accordance with exemplary embodiments.
[0019] Fla 10 is a block diagram illustrating a computer system architecture
in
accordance with exemplary ernbodiments.
[0020] Further areas of applicability of the present disclosure will become
apparent
from the detailed description provided hereinafter. It should be understood
that the
detailed description of exemplary embodiments are intended for illustration
purposes only and are, therefore, not intended to necessarily limit the scope
of the
disclosure.
DETAILED DESCRIPTION
Definition of Terms
[0021] Payment Network ¨ A system or network used for the transfer of money
via
the use of cash-substitutes. Payment networks may use a variety of different
protocols and procedures in order to process the transfer of money for various
types of transactions. Transactions that may be performed via a payment
network
may include product or service purchases, credit purchases, debit
transactions,
fund transfers, account withdrawals, etc. Payment networks may be configured
to
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perform transactions via cash-substitutes, which may include payment cards,
letters
of credit, checks, financial accounts, etc. Examples of networks or systems
configured to perform as payment networks include those operated by
MasterCard , VISA , Discover , American Express , etc.
(0022] Payment Account ¨ A financial account that may be used to fund a
transaction, such as a checking account, savings account, credit account,
virtual
payment account, etc. A payment account may be associated with an entity,
which
may include a person, family, company, corporation, governmental entity, etc.
In
some instances, a payment account may be virtual, such as those accounts
operated by PayPal , etc.
[0023] Payment Card ¨ A card or data associated with a payment account that
may
be provided to a merchant in order to fund a financial transaction via the
associated
payment account. Payment cards may include credit cards, debit cards, charge
cards, stored-value cards, prepaid cards, fleet cards, virtual payment
numbers,
virtual card numbers, controlled payment numbers, etc. A payment card may be a
physical card that may be provided to a merchant, or may be data representing
the
associated payment account (e.g., as stored in a communication device, such as
a
smart phone or computer). For example, in some instances, data including a
payment account number may be considered a payment card for the processing of
a transaction funded by the associated payment account. In some instances, a
check may be considered a payment card where applicable.
System for Generating and Provisioning Payment Credentials
[0024]FIG. 1 illustrates a system 100 for the identification of fraud factors
using
location data points based on home markets and consumer geographic locations.
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The system 100 may include a fraud factor processing server 102, discussed in
more detail below. The fraud factor processing server 102 may be configured to
identify home markets for consumers and to identify fraud factors based on
consumer home markets and identified geographic locations at predetermined
times.
[0025]The fraud factor processing server 102 may receive transaction data
related
to a plurality of financial transactions from a financial transaction
processing agency
104. The financial transaction processing agency 104 may operate as or as a
part
of a payment network, or may be an entity that receives and stores transaction
information received from a payment network. The financial transaction
processing
agency 104 may transmit transaction data to the fraud factor processing server
102, In some embodiments, the transaction data may lack personally
identifiable
information. In other embodiments, the transaction data may be personally
identifiable, but obtained with the consent of the consumer involved in the
corresponding financial transaction.
[0026]The fraud factor processing server 102 may also receive data points from
one or more data providers 106. The data points may be points of data
associated
with a plurality of consumers that are related to information that may be
considered
in the creation of one or more fraud factors, such as credit information,
fraud
history, payment account information, etc. Information suitable for use as
data
points will be apparent to persons having skill in the relevant art. A fraud
factor
may be a factor identified by the fraud factor processing server 102 that may
be
used as or as part of a fraud rule or fraud model used to identify the
likelihood that
a payment transaction may be fraudulent. The use of fraud factors in the
creation
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of fraud rules and/or fraud models and the application thereof to a payment
transaction will be apparent to persons having skill in the relevant art.
[0027]The fraud factor processing server 102 may store a plurality of consumer
data entries in a consumer database, discussed in more detail below, where
each
consumer data entry is associated with a consumer and includes at least a
mobile
device identifier and an account identifier. The mobile device identifier may
identify
a mobile device 108 associated with the consumer. The mobile device 108 may be
any type of mobile computing device suitable for performing the functions as
disclosed herein, such as a cellular phone, smart phone, tablet computer, etc.
[0028]The fraud factor processing server 102 may identify at least one home
market (e.g., a geographic region associated with a defined set of activities,
or
geofence, in this instance associated with a statistical model of transactions
within
a range of a person's home, that might be set statically, customized to the
person
or dynamically set according to activities or a person, group or panel of
people or a
population in general), e.g., for each consumer based on usage patterns of a
financial account (e.g., a payment card account) associated with the account
identifier included in the corresponding consumer data entry. In some
embodiments, the usage patterns may be identified using the transaction
history
received from the financial transaction processing agency 104. Home markets,
discussed in more detail below, may be a geographic area in which the consumer
regularly transacts using the financial account. This can be determined by
frequency, type of transactions (grocery or home improvement, as examples),
statistical models of populations (e.g., people in a city might have a home
market of
a few blocks of a home address and in the suburb several miles based of
statistical
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models of a panel or the general population, or as determined for a particular
individual). In some instances, a consumer may have multiple home markets. For
example, a consumer may have a first home market centered on their primary
residence, a second home market centered on their place of business, and a
third
home market centered around a vacation property, or a place of work if they
tend to
spend regularly in that location.
[0029] The system 100 may further include a location identifying agency 112.
The
location identifying agency 112 may be configured to identify a geographic
location
of the mobile device 108 or by the mobile device self-reporting its location
(e.g., at
the consent of the user of the mobile device 108). Methods for identifying a
geographic location of a mobile device will be apparent to persons having
skill in
the relevant art, such as using the global positioning system. In some
embodiments, the location identifying agency 112 may identify the geographic
location at a predetermined time or at predetermined time intervals. For
example,
the location identifying agency 112 may identify the geographic location of
the
mobile device 108 every four hours, or at set times throughout the day. In
such an
instance, the identification of the geographic location of a plurality of
mobile devices
may be performed using less bandwidth and resources, while still providing an
adequate sample of data.
(0030]The location identifying agency 112 may transmit the identified
geographic
location of the mobile device 108 to the fraud factor processing server 102.
The
fraud factor processing server 102 may then compare the identified geographic
location with the identified at least one home market of the consumer
corresponding to the mobile device 108 to determine if the mobile device 108,
and
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therefore consumer, were outside of their home market(s). If the geographic
location was outside of each home market, then the fraud factor processing
server
102 may identify (e.g., generate) a location data point including the
geographic
location, the consumer, and a time and/or date at which the geographic
location
was identified and store the identified location data point in a fraud
database.
[0031]The fraud factor processing server 102 may then identifying at least one
fraud factor for detecting fraud in a payment transaction, where the at least
one
fraud factor is based on at least the plurality of financial transactions
involving the
consumer, the plurality of data points involving the consumer, and each
identified
location data point. In some embodiments, the fraud factor processing server
102
may further identify a fraud rule and/or a fraud model based on at least the
identified at least one fraud factor.
[0032]The identified fraud factor may be used to assist in the identification
of
fraudulent transactions. For example, the consumer corresponding to the mobile
device 108 may initiate a payment transaction with a merchant 110. The
merchant
110 may be located outside of a home market of the consumer. The merchant 110
may submit an authorization request for the payment transaction to the
financial
transaction processing agency 104 for processing using systems and methods
apparent to persons having skill in the relevant art. The financial
transaction
processing agency 104 may transmit relevant transaction information to the
fraud
factor processing server 102. The fraud factor processing server 102 may then
identify a fraud score for the payment transaction based on the transaction
information and identified fraud factor (e.g., and/or identified fraud rule or
model)
indicating the likelihood that the transaction is fraudulent, and may return
the score
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to the financial transaction processing agency 104. The financial transaction
processing agency 104 (e.g., or an issuer of a payment account used in the
financial transaction) may then use the score to determine if the transaction
should
be authorized.
[0033] In the present example, the fraud factor may identify a score
indicating that
the transaction is not fraudulent based on identified location data points.
The fraud
factor processing server 102 may have identified location data points showing
that
the mobile device 108 associated with the consumer was outside of their home
market and at or near the same geographic location as the merchant 110 at
around
the same time for each of the last three days, and that the geographic
location is
near a main road connecting their residence home market with their place of
business home market. In such an instance, the fraud factor processing server
102
may identify a low likelihood of a fraudulent transaction as the data
indicates the
consumer has simply started to stop and shop at the merchant 110 regularly on
their way to or from work.
Fraud Factor Processing Server
[0034] FIG. 2 illustrates an embodiment of the fraud factor processing server
102 of
the system 100. It will be apparent to persons having skill in the relevant
art that
the embodiment of the fraud factor processing server 102illustrated in FIG. 2
is
provided as illustration only and may not be exhaustive to all possible
configurations of the fraud factor processing server 102suitable for
performing the
functions as discussed herein. For example, the computer system 1000
illustrated
in FIG. 10 and discussed in more detail below may be a suitable configuration
of
the fraud factor processing server 102.
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[0035] The fraud factor processing server 102 may include at least a receiving
unit
202, a processing unit 204, a transaction database 206, a fraud database 208,
a
consumer database 210, and a transmitting unit 212. The receiving unit 202 may
be configured to receive transaction data for a plurality of financial
transactions.
The processing unit 204 may store the received transaction data in the
transaction
database 206. The transaction data may include any information suitable for
performing the functions as disclosed herein as will be apparent to persons
having
skill in the relevant art. For example, the transaction data may include
information
identifying the consumer involved in the financial transaction (e.g., at the
consent of
the consumer), time and/or date information, and merchant identifying
information
and/or geographic location identifying information.
[0036] The receiving unit 202 may also be configured to receive data points
from
one or more data providers 106, such as a credit bureau. The processing unit
204
may store the received data points in the fraud database 208. The processing
unit
204 may also be configured to store, in the consumer database 210, a plurality
of
consumer data entries, wherein each consumer data entry is related to a
consumer.
The consumer database 210 and consumer data entries are discussed in more
detail below. In some embodiments, the receiving unit 202 may receive consumer
information, such as a mobile device identifier, from which consumer data
entries
may be created and stored. For example, a consumer may register with the fraud
factor processing server 102 (e.g., or be registered via an issuer) and
provide a
mobile device identifier.
[0037] The processing unit 204 may be further configured to identify, for each
consumer data entry, at least one home market based on usage patterns of a
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financial account associated with the consumer for each corresponding consumer
data entry. Home markets are discussed in more detail below. The receiving
unit
202 may also be configured to receive geographic location information, such as
from the location identifying agency 112. The processing unit 204 may be
configured to store, in the fraud database 208, a location data point for each
received geographic location if the received geographic location is outside of
the
home market(s) for the corresponding consumer as stored in the consumer data
entry. The location data point may further include the corresponding consumer
and
a time and/or date at which the geographic location was identified.
[0038] The processing unit 204 may be further configured to identify at least
one
fraud factor for detecting fraud in a financial transaction based on the
information
included in the transaction database 206 and the fraud database 208. In some
embodiments, the processing unit 204 may be further configured to identify
and/or
generate at least one fraud rule or fraud model including the identified at
least one
fraud factor.
[0039] In one embodiment, the transmitting unit 212 may be configured to
transmit
the consumer data entries to the location identifying agency 112. In such an
embodiment, the location identifying agency 112 may identify location data
points
where the mobile device 108 associated with each consumer corresponding to a
consumer data entry is identified at a geographic location outside of the
associated
home market(s). The location identifying agency 112 may then transmit the
identified location data points to the fraud factor processing server 102 to
be
received by the receiving unit 202, and then stored in the fraud database 208
by
the processing unit 204.
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[0040] In some embodiments, the fraud factor processing server 102 may operate
as part of or in conjunction with a payment network, such as one including the
financial transaction processing agency 104. In one such embodiment, the
receiving unit 202 may receive transaction information for a payment
transaction,
the processing unit 204 may identify a fraud score for the payment transaction
based on the identified at least one fraud factor, and the transmitting unit
212 may
transmit a response including the identified fraud score.
[0041] In another such embodiment, the receiving unit 202 may receive an
authorization request for a payment transaction, wherein the authorization
request
includes transaction information. The processing unit 204 may identify a fraud
score for the payment transaction based on at least the transaction
information and
identified at least one fraud factor or a fraud rule or model incorporating
the at least
one fraud factor. The transmitting unit 212 may transmit the fraud score and
relevant transaction information to an issuer corresponding to a payment
account
used to fund the payment transaction, and the receiving unit 202 may receive a
response from the issuer indicating approval or denial of the payment
transaction.
The processing unit 204 may then generate an authorization response, which may
be transmitted by the transmitting unit 212 to the originator of the
authorization
request (e.g., the merchant 110 or an acquirer, such as an acquiring bank).
Consumer Database and Home Markets
[0042] FIG. 3 illustrates the consumer database 210 of the fraud factor
processing
server 102. The consumer database 210 may store a plurality of consumer data
entries 302, illustrated in FIG. 3 as consumer data entries 302a, 302b, and
302c.
Each consumer data entry 302 may include at least a mobile device identifier
304,
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an account identifier 306, a location frequency 308, and at least one home
market
310.
[0043] The mobile device identifier 304 may be a value suitable for
identifying a
unique mobile device 108 associated with a consumer corresponding to the
consumer data entry 302. Values suitable for use as the mobile device
identifier
304 will be apparent to persons having skill in the relevant art and may
include, for
example, a phone number, a media access control (MAC) address, an internet
protocol (IP) number, email address, etc. The account identifier 306 may be a
value suitable for identifying a payment account associated with the consumer
corresponding to the consumer data entry 302. The account identifier 306 may
be,
for example, a payment account number (e.g., a credit card number), a
username,
a phone number, an e-mail address, or any other suitable value as will be
apparent
to persons having skill in the relevant art.
[0044] The locating frequency 308 may be one or more predetermined periods of
time at which the geographic location of the mobile device 108 corresponding
to the
mobile device identifier 304 is to be identified. In some instances, the
locating
frequency 308 may be a time interval, such as every four hours. In other
instances,
the locating frequency 308 may be specified recurring times, such as at 8
A.M.,
noon, 2 P.M., 4 P.M., 6 P.M., and 10 P.M. In some embodiments, the consumer
and/or the fraud factor processing server 102 may specify different locating
frequencies 308 for different days (e.g., each day of the week, weekends,
weekdays, etc.).
[0045] The home market 310 (e.g., a home geofence) may be a geographic area
where the consumer corresponding to the consumer data entry 302 regularly
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transacts based on usage patterns for the payment account corresponding to the
account identifier 306. FIG. 4 illustrates multiple consumer data entries 302
and
the home markets 310 included therein. As illustrated in FIG. 4, in some
embodiments a home market 310 may be defined by a center 402, such as the
center of a postal code, and may include a market distance 312, which may be a
radius or diameter from the center 402 that represents the area covered by the
home market 310. The home market 310 may be represented as a circle at the
center 402 with a radius of the distance 312, may be a square with the center
402
and the distance 312 being the distance to one side or one corner of the
square,
etc.
[0046] In some embodiments, the home market 310 may be defined as one or more
defined geographic areas, such as a postal code, zip code, subdivision,
neighborhood, county, city, municipality, etc. In other embodiments, the
center 402
may be a street or physical address, a geographic location defined by
longitude
and latitude, or any other suitable value. In some instances, the home market
310
may be any type of shape defined by discrete points or values (e.g., such as
street
addresses or geographic coordinates). Suitable methods for determining and
defining home markets 310 will be apparent to persons having skill in the
relevant
art and can be adapted to fit circumstances, business needs, fraud models or
rules,
or be based on history or statistical studies of the customer, grouped of
customers
perhaps clustered by home address or other demographic information, somewhat
arbitrary, based on population density (urban home markets would likely be
geographically smaller than urban markets, for instance), and other factors or
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considerations, whether commercial, to adjust computer overhead, or any other
appropriate factor or consideration.
[0047] Some consumer data entries 302 may include multiple home markets 310,
such as the consumer data entries 302a and 302b illustrated in FIG. 4. Such an
instance may occur when, for example, a consumer regularly transacts nearby
their
residence as well as near to their place of employment, which may be a
significant
commute away from their residence. A consumer may also have a home market
located at a regular travel destination, or in other instances apparent to
persons
having skill in the relevant art. In some cases, home markets 310 may be
different
for specific days of the week, months, seasons, etc. or certain home markets
310
may be active for specific days of the week, months, seasons, etc. For
example, a
work home market 310 may not be considered a home market for the
identification
of location data points on weekends.
Identification of Location Data Points
[0048] FIG. 6 illustrates a method 600 for the identification of location data
points
based on the geographic location of a mobile device 108 compared to one or
more
home markets 310 corresponding to a consumer associated with the mobile device
108.
[0049] In step 602, a mobile device identifier 304 of a consumer data entry
302 may
be identified. In step 604, the fraud factor processing server 102 and/or
location
identifying agency 112 may determine if the current time corresponds to the
predetermined time as indicated by the locating frequency 308. If it is not
the
predetermined time, then, in step 606, the method may continue to wait until
the
predetermined time is reached. Once it is the predetermined time, then, in
step
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608, the location identifying agency 112 may locate the mobile device 108
corresponding to the mobile device identifier 304. Suitable methods for
locating the
mobile device 108 will be apparent to persons having skill in the relevant
art.
[0050] FIG. 5 illustrates a plurality of location data points 502. As
illustrated in FIG.
5, each location data point 502 may include the mobile device identifier 304,
a
geographic location 504, and a time and/or date 506. The geographic location
504
may be a postal code, street address, geographic coordinate, or any other
suitable
value. In step 610, the fraud factor processing server 102 or location
identifying
agency 112 may determine if the geographic location 504 identified in step 608
is
located within a home market 310.
[0051] If the geographic location 504 is located within a home market 310,
then
method 600 returns to step 606 and waits for the next predetermined time to
locate
the mobile device 108. lf, on the other hand, the geographic location 504 is
not
located within a home market 310, then, in step 612, the time and/or date 506
for
the location data point 502 is stamped. As illustrated in FIG. 5, in the first
location
data point 502, the mobile device identifier 304 corresponds to the consumer
data
entry 302a illustrated in FIG. 4, which includes three home markets 310. The
geographic location 504 is not located within the distance 312 to the center
402 of
any of the three home markets 310. As a result, the fraud factor processing
server
102 stores, in step 614, a the location data point 502 in the fraud database
208
including the mobile device identifier 304, the geographic location 504, and
the time
and/or date 506 as the mobile device 108 was identified outside of any
associated
home markets 310 at the stamped time and/or date 506.
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Identification of Fraud Factors
[0052] FIG. 7 illustrates a method 700 for the identification, testing, and
application
of fraud factors based on the identification of location data points 502.
[0053]In step 702, the fraud factor processing server 102 may determine if
there
are any mobile device identifiers 304 that need to have home markets
identified. If
there are, then, in step 704, the processing unit 204 may identify the next
mobile
device identifier 304 and identify at least one corresponding home market 310
based on usage patterns of a payment account corresponding to the account
identifier 306 included in the corresponding consumer data entry 302. In step
706,
the processing unit 204 may associate the identified at least one home market
310
with the mobile device 108 by storing the at least one home market 310 in the
consumer data entry 302 including the mobile device identifier 304.
[0054]Then, once all mobile device identifiers 304 have been processed (e.g.,
each mobile device identifier 304 has associated home market(s) 310), in step
708
the consumer data entries 302 may be transmitted to the location identifying
agency 112. The location identifying agency 112 may, based on the information
included in the consumer data entries 302, identifying a plurality of location
data
points 502 and may transmit the identified location data points 502 to the
fraud
factor processing server 102, which may receive the plurality of location data
points
in step 710. It will be apparent to persons having skill in the relevant art
that, in
some embodiments, the location identifying agency 112 may provide geographic
location information 504 to the fraud factor processing server 102, and the
fraud
factor processing server 102 may identify the location data points 502 based
thereon.
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[0055] In step 712, the fraud factor processing server 102 may store the
plurality of
location data points 502 in the fraud database 208. Then, in step 714, the
fraud
factor processing server 102 may identify and test at least one fraud factor
based
on transaction information stored in the transaction database 206 and the data
points, including location data points, stored in the fraud database 208
associated
with each consumer corresponding to each consumer data entry 302. Methods for
testing a fraud factor will be apparent to persons having skill in the
relevant art and
may include, for example, applying the fraud factor to a fraud rule or model
and
scoring a known fraudulent transaction to determine if the transaction is
considered
to be fraudulent once the fraud factor is applied.
[0056] In step 716, the fraud factor processing server 102 may determine if
the
fraud factor is suitable based on at least the test performed in step 714. If
the fraud
factor is not suitable, then the process may return to step 714 where a new or
modified fraud factor is identified and tested based on the location data
points 502
and additional information as discussed above. If the fraud factor is
suitable, then,
in step 718, the fraud factor may be used (e.g., as part of a fraud rule or
fraud
model) in the processing of payment transactions to identify potentially
fraudulent
transactions. Methods for applying a fraud factor singularly or as part of a
fraud
rule or fraud model to payment transactions during processing will be apparent
to
persons having skill in the relevant art.
First Exemplary Method for Identifying Fraud Factors
[0057] FIG. 8 illustrates a method 800 for identifying fraud factors using
home
markets 310 and location data points 502.
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[0058] In step 802, a plurality of data points may be stored in a fraud
database
(e.g., the fraud database 208). In some embodiments, the plurality of data
points
may include at least one of: credit information, transaction history, fraud
history,
and financial account information. In step 804, a plurality of financial
transactions
may be stored in a transaction database (e.g., the transaction database 206).
In
some embodiments, the plurality of financial transactions may include at least
one
financial transaction involving the consumer associated with each consumer
data
entry 302 in the plurality of consumer data entries.
[0059] In step 806, a plurality of consumer data entries may be stored in a
consumer database (e.g., the consumer database 210), wherein each consumer
data entry (e.g., the consumer data entry 302) is associated with a consumer
and
includes at least a mobile device identifier (e.g., the mobile device
identifier 304)
and an account identifier (e.g., the account identifier 306). In step 808, at
least one
home market (e.g., the home market 310) may be identified, by a processing
device
(e.g., the processing unit 204) for each consumer data entry 302 in the
plurality of
consumer data entries based on usage patterns for a financial account
associated
with the account identifier 306. In some embodiments, the financial account
associated with the account identifier 306 is a payment card account. In a
further
embodiment, the usage patterns may include location data for financial
transactions
funded by a payment card associated with the payment card account. In one
embodiment, the at least one home market 310 may be represented by at least
one
of: a postal code, longitude and latitude, and a street address.
[0060] In step 810, the identified at least one home market 310 may be
associated,
in the consumer database 210, with the corresponding consumer data entry 302.
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In step 812, a geographic location (e.g., the geographic location 504) may be
identified, at a predetermined time, for a mobile device (e.g., the mobile
device 108)
associated with the mobile device identifier 304 for each consumer data entry
302.
In one embodiment, the geographic location 504 may be represented using
longitude and latitude. In some embodiments, each consumer data entry 302 may
further include at least a checking time, wherein the predetermined time is
the
checking time for the corresponding consumer data entry 302.
[0061] In step 814, a location data point (e.g., the location data point 502)
may be
stored, in the fraud database 208, for each identified geographic location 504
if the
identified geographic location 504 is outside of a predetermined distance
(e.g., the
market distance 512) from the at least one home market 310 associated with the
corresponding consumer data entry 302, wherein the location data point 502
includes at least the identified geographic location 504, the consumer
associated
with the corresponding consumer data entry 302, and a time and/or date (e.g.,
the
time and/or date 506) at which the geographic location 504 was identified. In
one
embodiment, identifying the at least one home market 310 may further include
identifying a market distance (e.g., the market distance 312) associated with
the at
least one home market 310, wherein the predetermined distance is the
associated
market distance 312.
[0062] In step 816, at least one fraud factor for detecting fraud in a
financial
transaction may be identified, by the processing device 204, wherein the at
least
one fraud factor is based on at least the plurality of financial transactions,
the
plurality of data points, and each location data point 502. In one embodiment,
the
method 800 may further include: receiving, by a receiving device (e.g., the
receiving
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unit 202), transaction information for a financial transaction; identifying,
by the
processing device 204, a fraud score based on the transaction information and
the
at least one fraud factor; and transmitting, by a transmitting device (e.g.,
the
transmitting unit 212), the identified fraud score.
[006331n some embodiments, the method 800 may be used as part of a method for
processing a financial transaction. In such a method, an authorization request
for a
financial transaction may be received by the receiving device 202, wherein the
authorization request includes transaction information. A fraud score may be
identified for the financial transaction based on the transaction information
and at
least one fraud factor, wherein the fraud factor is identified using the
method 800.
The transaction information and identified fraud score may be transmitted to
an
issuer, and then an indication of approval of the financial transaction may be
received by the receiving device 202. The processing device 204 may then
process the financial transaction, which may include transmitting an
authorization
response message.
Second Exemplary Method for Identifying Fraud Factors
[0064] FIG. 9 illustrates a method 900 for identifying fraud factors using
home
markets 310 and location data points 502.
[0065] In step 902, a plurality of data points may be stored in a fraud
database
(e.g., the fraud database 208). In one embodiment, the plurality of data
points may
include at least one of: credit information, transaction history, fraud
history, and
financial account information. In step 904, a plurality of financial
transactions may
be stored in a transaction database (e.g., the transaction database 206). In
one
embodiment, the plurality of financial transactions may include at least one
financial
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transaction involving the consumer associated with each consumer data entry
302
in the plurality of consumer data entries.
[0066] In step 906, a plurality of consumer data entries may be stored in a
consumer database (e.g., the consumer database 210), wherein each consumer
data entry (e.g., the consumer data entry 302) is associated with a consumer
and
includes at least a mobile device identifier (e.g., the mobile device
identifier 304)
and at least one home market (e.g., the home market 310). In one embodiment,
each of the at least one home market 310 may be represented by at least one
of: a
postal code, longitude and latitude, and a street address.
[0067] In step 908, a transmitting device (e.g., the transmitting unit 212)
may
transmit the plurality of consumer data entries.
[0068] In step 910, a plurality of location data points may be received by a
receiving
device (e.g., the receiving unit 202), wherein each location data point (e.g.,
the
location data point 502) includes at least a mobile device identifier 304, a
geographic location (e.g., the geographic location 504) of a mobile device
(e.g., the
mobile device 108) associated with the mobile device identifier 304, and a
time
and/or date (e.g., the time and/or date 506) at which the mobile device 108 is
identified as outside of a predetermined distance from the at least one home
market 310 associated with the corresponding consumer data entry. In one
embodiment, the geographic location may be represented using longitude and
latitude. In some embodiments, each of the at least one home market 310 may be
associated with a market distance (e.g., the market distance 312), and the
predetermined distance may be the associated market distance 312.
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[0069] In step 912, at least one fraud factor for detecting fraud in a
financial
transaction may be identified by the processing device 204, wherein the at
least
one fraud factor is based on at least the plurality of financial transactions,
the
plurality of data points, and each location data point 502. In one embodiment,
the
method 900 may further include: receiving, by the receiving device 202,
transaction
information for a financial transaction; identifying, by the processing device
204, a
fraud score based on the transaction information and the identified at least
one
fraud factor; and transmitting, by the transmitting device 212, the identified
fraud
score.
[0070] In some embodiments, the method 900 may be used as part of a method for
processing a financial transaction. In such a method, an authorization request
for a
financial transaction may be received by the receiving device 202, wherein the
authorization request includes transaction information. A fraud score may be
identified for the financial transaction based on the transaction information
and at
least one fraud factor, wherein the fraud factor is identified using the
method 900.
The transaction information and identified fraud score may be transmitted to
an
issuer, and then an indication of approval of the financial transaction may be
received by the receiving device 202. The processing device 204 may then
process the financial transaction, which may include transmitting an
authorization
response message.
Computer System Architecture
[0071] FIG. 10 illustrates a computer system 1000 in which embodiments of the
present disclosure, or portions thereof, may be implemented as computer-
readable
code. For example, the fraud factor processing server 102, the financial
transaction
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processing agency 104, the data provider 106, the mobile device 108, the
merchant
110, and the location identifying agency 112 of FIG. 1 may be implemented in
the
computer system 1000 using hardware, software, firmware, non-transitory
computer readable media having instructions stored thereon, or a combination
thereof and may be implemented in one or more computer systems or other
processing systems. Hardware, software, or any combination thereof may embody
modules and components used to implement the methods of FIGS. 6-9.
[0072] If programmable logic is used, such logic may execute on a commercially
available processing platform or a special purpose device. A person having
ordinary skill in the art may appreciate that embodiments of the disclosed
subject
matter can be practiced with various computer system configurations, including
multi-core multiprocessor systems, minicomputers, mainframe computers,
computers linked or clustered with distributed functions, as well as pervasive
or
miniature computers that may be embedded into virtually any device. For
instance,
at least one processor device and a memory may be used to implement the above
described embodiments.
[0073] A processor device as discussed herein may be a single processor, a
plurality of processors, or combinations thereof. Processor devices may have
one
or more processor "cores." The terms "computer program medium," "non-
transitory
computer readable medium," and "computer usable medium" as discussed herein
are used to generally refer to tangible media such as a removable storage unit
1018, a removable storage unit 1022, and a hard disk installed in hard disk
drive
1012.
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[0074] Various embodiments of the present disclosure are described in terms of
this
example computer system 1000. After reading this description, it will become
apparent to a person skilled in the relevant art how to implement the present
disclosure using other computer systems and/or computer architectures.
Although
operations may be described as a sequential process, some of the operations
may
in fact be performed in parallel, concurrently, and/or in a distributed
environment,
and with program code stored locally or remotely for access by single or multi-
processor machines. In addition, in some embodiments the order of operations
may
be rearranged without departing from the spirit of the disclosed subject
matter.
[0075] Processor device 1004 may be a special purpose or a general purpose
processor device. The processor device 1004 may be connected to a
communication infrastructure 1006, such as a bus, message queue, network,
multi-
core message-passing scheme, etc. The network may be any network suitable for
performing the functions as disclosed herein and may include a local area
network
(LAN), a wide area network (WAN), a wireless network (e.g., WiFi), a mobile
communication network, a satellite network, the Internet, fiber optic, coaxial
cable,
infrared, radio frequency (RF), or any combination thereof. Other suitable
network
types and configurations will be apparent to persons having skill in the
relevant art.
The computer system 1000 may also include a main memory 1008 (e.g., random
access memory, read-only memory, etc.), and may also include a secondary
memory 1010. The secondary memory 1010 may include the hard disk drive 1012
and a removable storage drive 1014, such as a floppy disk drive, a magnetic
tape
drive, an optical disk drive, a flash memory, etc.
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[0076]The removable storage drive 10'14 may read from and/or write to the
removable storage unit 1018 in a well-known manner. The removable storage unit
1018 may include a removable storage media that may be read by and written to
by
the removable storage drive 1014. For example, if the removable storage drive
1014 is a floppy disk drive, the removable storage unit 1018 may be a floppy
disk.
In one embodiment, the removable storage unit 1018 may be non-transitory
computer readable recording media.
[007711n some embodiments, the secondary memory 1010 may include alternative
means for allowing computer programs or other instructions to be loaded into
the
computer system 1000, for example, the removable storage unit 1022 and an
interface 1020. Examples of such means may include a program cartridge and
cartridge interface (e.g., as found in video game systems), a removable memory
chip (e.g., EEPROM, PROM, etc.) and associated socket, and other removable
storage units 1022 and interfaces 1020 as will be apparent to persons having
skill
in the relevant art.
[0078] Data stored in the computer system 1000 (e.g., in the main memory 1008
and/or the secondary memory 1010) may be stored on any type of suitable
computer readable media, such as optical storage (e.g., a compact disc,
digital
versatile disc, Blu-ray disc, etc.) or magnetic tape storage (e.g., a hard
disk drive).
The data may be configured in any type of suitable database configuration,
such as
a relational database, a structured query language (SQL) database, a
distributed
database, an object database, etc. Suitable configurations and storage types
will
be apparent to persons having skill in the relevant art.
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[0079] The computer system 1000 may also include a communications interface
1024. The communications interface 1024 may be configured to allow software
and
data to be transferred between the computer system 1000 and external devices.
Exemplary communications interfaces 1024 may include a modem, a network
interface (e.g., an Ethernet card), a communications port, a PCMCIA slot and
card,
etc. Software and data transferred via the communications interface 1024 may
be
in the form of signals, which may be electronic, electromagnetic, optical, or
other
signals as will be apparent to persons having skill in the relevant art. The
signals
may travel via a communications path 1026, which may be configured to carry
the
signals and may be implemented using wire, cable, fiber optics, a phone line,
a
cellular phone link, a radio frequency link, etc.
[0080] Computer program medium and computer usable medium may refer to
memories, such as the main memory 1008 and secondary memory 1010, which
may be memory semiconductors (e.g. DRAMs, etc.). These computer program
products may be means for providing software to the computer system 1000.
Computer programs (e.g., computer control logic) may be stored in the main
memory 1008 and/or the secondary memory 1010. Computer programs may also
be received via the communications interface 1024. Such computer programs,
when executed, may enable computer system 1000 to implement the present
methods as discussed herein. In particular, the computer programs, when
executed, may enable processor device 1004 to implement the methods
illustrated
by FIGS. 6-9, as discussed herein. Accordingly, such computer programs may
represent controllers of the computer system 1000. Where the present
disclosure
is implemented using software, the software may be stored in a computer
program
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product and loaded into the computer system 1000 using the removable storage
drive 1014, interface 1020, and hard disk drive 1012, or communications
interface
1024.
[0081]Techniques consistent with the present disclosure provide, among other
features, systems and methods for the identification of fraud factors. While
various
exemplary embodiments of the disclosed system and method have been described
above it should be understood that they have been presented for purposes of
example only, not limitations. It is not exhaustive and does not limit the
disclosure
to the precise form disclosed. Modifications and variations are possible in
light of
the above teachings or may be acquired from practicing of the disclosure,
without
departing from the breadth or scope.
